Lab 2 loading library

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(MASS)
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:MASS':
## 
##     select
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(xlsx)

Reading input

olive_data <- read.csv("olive.csv")
olive_data <- olive_data[,-1]

olive_data$Region <- factor(olive_data$Region, levels = c(1,2,3))

1. Scatter plot dependence of Palmitic on Oleic, linolenic not classed and classed

ggplot(data = olive_data, aes(x = oleic, y = palmitic), colour = linolenic) + geom_point(aes(colour = linoleic))

ggplot(data = olive_data, aes(x = oleic, y = palmitic)) + geom_point(aes(colour = cut_interval(linoleic, n = 4)))

Analysis: The second graph is easier to analysis than compared to first one, the perception problem highlighted here is the difference in the channel capacity of human preception. Where capacity of distingushing hue > intensity

2. Scatter plot dependence of Palmitic on Oleic, linolenic split by different attributes

ggplot(data = olive_data, aes(x = oleic, y = palmitic)) + geom_point(aes(colour = cut_interval(linolenic, n = 4)))

ggplot(data = olive_data, aes(x = oleic, y = palmitic)) + geom_point(aes(size = cut_interval(linolenic, n = 4)))
## Warning: Using size for a discrete variable is not advised.

ggplot(data = olive_data, aes(x = oleic, y = palmitic)) + geom_point() +  geom_spoke(aes(angle = as.numeric(cut_interval(linolenic, n = 4))*10), radius = 50)

Analysis: The discretized Linolenic with color was the easiest to detect boundary. This is becuase the channel capacity of detection is in the order of color>direction>size

3. Scatter plot dependence of Oleic on Eicosenoic, Region split by different attributes

ggplot(data = olive_data, aes(x = oleic, y = eicosenoic)) + geom_point(aes(colour = as.numeric(Region)))

ggplot(data = olive_data, aes(x = oleic, y = eicosenoic)) + geom_point(aes(colour = Region))

Analysis: Using a factor simply as number assumes that the difference between region is an increment of one, eg: Cat->Dog->Human the difference is one unit among the three, while treating them as factor does not assuming any step increment assumption. The Preattentive pattern emerged here due to distinct colour.

4. Scatter plot dependence of Oleic on Eicosenoic, linoleic not classed and classed and 27 objects

ggplot(data = olive_data, aes(x = oleic, y = eicosenoic)) + geom_point(aes(colour = cut_interval(linoleic, n = 3), shape = cut_interval(palmitic, n = 3), size = cut_interval(palmitoleic, n = 3)))
## Warning: Using size for a discrete variable is not advised.

Analysis: Its very hard to distinguish between 27 types of combination due to no clear boundary between the regions, the perception problem demostrated here is the attentive mechanism and no individual feature of the chart (shape, color, size) helps in distinguish the boundary

5. Scatter plot dependence of Oleic on Eicosenoic, linoleic not classed and classed and 27 objects

ggplot(data = olive_data, aes(x = oleic, y = eicosenoic)) + geom_point(aes(colour = Region, shape = cut_interval(palmitic, n = 3), size = cut_interval(palmitoleic, n = 3)))
## Warning: Using size for a discrete variable is not advised.

Analysis: Due to establishment of viusally clear boundary between the regions, the attentive mechanism has no problems scanning through the individual feature of the chart (shape, color, size) which inturns helps in distinguish the boundary

6. piechart of oils vs. region

p <- (olive_data) %>% group_by(Region) %>% summarize(total_oils = sum(palmitic, palmitoleic, stearic, oleic, linoleic, linolenic, arachidic, eicosenoic)) %>% plot_ly(values = ~total_oils, type = 'pie', showlegend = FALSE) %>% layout(title = 'Total oils by region',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
p

Analysis: The plot does not conform to the practices under good visualization, it is generally not advised to use piechart because angles are harder to detect than size. Not having clear lables/legend simply adds to more overhead to process the information

7. 2D density contour plot

ggplot(olive_data, aes(x = linoleic, y = eicosenoic)) + geom_point(aes(colour = Region)) + geom_density_2d()

ggplot(olive_data, aes(x = linoleic, y = eicosenoic)) + geom_point(aes(colour = Region))

Analysis: As evident from the two plots the density plot suggests a clusters being formed while the simple scatter plots coloured by region suggests a simpler cluster

Assignment 2

Loading the input xlxs file

baseball <- read.xlsx("baseball-2016.xlsx", sheetName = "Sheet1")

Analysis: Yes its reasonable to scale the data (perform dimensionality reduction) since more than visualizing more than 4 feaures is not advised.

non mertic MDS

distance <- dist(baseball, method = "minkowski")
## Warning in dist(baseball, method = "minkowski"): NAs introduced by coercion
fit <- isoMDS(distance, k = 2)
## initial  value 12.061782 
## final  value 12.060974 
## converged
fit
## $points
##             [,1]        [,2]
##  [1,]  174.54938  131.124243
##  [2,] -182.40140 -117.454473
##  [3,]  124.90457   57.401223
##  [4,]  488.11041  -97.048987
##  [5,]  120.04189   72.909394
##  [6,]  -32.45939  -38.883384
##  [7,]  -90.57921  -27.682332
##  [8,]   76.27979  -33.403319
##  [9,]  353.96012   65.107149
## [10,]  133.47471   16.396883
## [11,]  -15.00169  164.933200
## [12,]  -87.21574 -122.073770
## [13,]  -90.05219 -323.147974
## [14,]  -47.81722   16.895979
## [15,]  -97.72915 -148.374660
## [16,] -268.29449  257.463231
## [17,]   69.46076  133.417708
## [18,]  -89.70618   10.940488
## [19,] -121.33746 -126.790799
## [20,] -164.22874 -181.456796
## [21,] -333.00331   35.372480
## [22,]  -35.77541   12.615108
## [23,] -299.53261  187.207321
## [24,]  140.32094    4.802752
## [25,]  -13.14084 -219.299090
## [26,]  173.05957   56.461511
## [27,]  -75.33719  196.606369
## [28,]  110.05093  -60.506235
## [29,]   29.23160   98.696348
## [30,]   50.16755  -22.229568
## 
## $stress
## [1] 12.06097
#plot of solution
x <- fit$points[,1]
y <- fit$points[,2]
plot(x, y, xlab="Coordinate 1", ylab="Coordinate 2",
  main="Nonmetric MDS of baseball data", type="n")
text(x, y, labels = row.names(baseball), cex=.7)